Adaptive recurrent flow map operator learning for reaction diffusion dynamics

TL;DR

This paper introduces DDOL-ART, a data-driven recurrent operator learning method for reaction-diffusion systems that achieves stable long-term predictions, out-of-distribution robustness, and reduced training costs without relying on physics-based residuals.

Contribution

The paper presents a novel adaptive recurrent training strategy for operator learning that enhances stability, generalization, and efficiency in reaction-diffusion dynamics modeling.

Findings

DDOL-ART remains stable over long rollouts and generalizes to different systems.

It is several times faster than physics-based operator learners.

DDOL-ART maintains robustness under out-of-distribution initial conditions.

Abstract

Reaction-diffusion (RD) equations underpin pattern formation across chemistry, biology, and physics, yet learning stable operators that forecast their long-term dynamics from data remains challenging. Neural-operator surrogates provide resolution-robust prediction, but autoregressive rollouts can drift due to the accumulation of error, and out-of-distribution (OOD) initial conditions often degrade accuracy. Physics-based numerical residual objectives can regularize operator learning, although they introduce additional assumptions, sensitivity to discretization and loss design, and higher training cost. Here we develop a purely data-driven operator learner with adaptive recurrent training (DDOL-ART) using a robust recurrent strategy with lightweight validation milestones that early-exit unproductive rollout segments and redirect optimization. Trained only on a single in-distribution toroidal Gaussian family over short horizons, DDOL-ART learns one-step operators that remain stable under long rollouts and generalize zero-shot to strong morphology shifts across FitzHugh-Nagumo (FN), Gray-Scott (GS), and Lambda-Omega (LO) systems. Across these benchmarks, DDOL-ART delivers a strong accuracy and cost trade-off. It is several-fold faster than a physics-based numerical-loss operator learner (NLOL) under matched settings, and it remains competitive on both in-distribution stability and OOD robustness. Training-dynamics diagnostics show that adaptivity strengthens the correlation between validation error and OOD test error performance, acting as a feedback controller that limits optimization drift. Our results indicate that feedback-controlled recurrent training of DDOL-ART generates robust flow-map surrogates without PDE residuals, while simultaneously maintaining competitiveness with NLOL at significantly reduced training costs.